Learning 3D Shapes.
Several works aim to predict the geometry of 3D shapes given several input images, by directly optimizing the vertices of a template mesh through differentiable projections or through fitting a network [1]}, [2]}, [3]}, [4]}, [5]}.
Other works use MLPs as a deep prior to the optimized mesh [6]}, [7]}, [5]}.
On the other hand, some methods try to learn the distribution of 3D meshes by optimizing 3D generators independent of how the meshes look when rendered, solely based on the available 3D data and heuristic regularizers [9]}, [10]}, [11]}, [12]}. Point cloud methods offer an alternative to the mesh complex topology by learning generative models on the point clouds themselves, by using an Auto Encoder [13]}, [14]} or a GAN framework [13]}, [16]}. The implicit representation paradigm offers an alternative to meshes for smooth and detailed shape representation. These methods learn a continuous implicit representation of shapes by learning the Signed Distance Functions or Occupancy of the object through MLPs [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}.
While we learn a 3D representation in this work, the scope focuses on the quality of the rendering from novel views and not 3D reconstruction.
